from keras import layers
from keras import models



model = models.Sequential()

model.add(layers.Conv2D(32,(3,3),activation='relu',input_shape=(28,28,1)))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64,(3,3),activation ='relu'))
model.add(layers.MaxPooling2D((2,2)))
model.add(layers.Conv2D(64,(3,3),activation='relu'))

model.add(layers.Flatten())

model.add(layers.Dense(64,activation='relu'))
model.add(layers.Dense(10,activation='softmax'))


print (model.summary())

from keras.datasets import mnist
from keras.utils import to_categorical

(train_images,train_labels),(test_images,test_labels) = mnist.load_data()

train_images = train_images.reshape((60000,28,28,1))
train_images = train_images.astype('float32')/255

test_images = test_images.reshape((10000,28,28,1))
test_images = test_images.astype('float32')/255

train_labels = to_categorical(train_labels)
test_labels = to_categorical(test_labels)

model.compile(optimizer='rmsprop',
               loss='categorical_crossentropy',
               metrics=['accuracy'])

model.fit(train_images,train_labels,epochs=5,batch_size=64)

model.save('./model')